Meta-analyses of molecular seafood studies identify the global distribution of legal and illegal trade in CITES-regulated European eels
Bibliographic record
Abstract
Authentication of seafood products by means of molecular techniques has relevance for food sustainability and security, as well as international trade regulation, linked to transparency in food manufacturing. We focus on the molecular detection of the depleted European eel Anguilla anguilla , a species for which strict international trade regulations are in place since 2010, in studies conducted outside Europe. We found thirteen studies from nine countries (Canada, China, Japan, Malaysia, Peru, Singapore, South Korea, Taiwan, and USA) for which, on average, 59 ± 28% of the 330 sequenced eel samples comprised European eel. Only China, Japan, South Korea, and USA reported the import of European eel in the years prior to sampling. The authentication of eel products demonstrates a global, in part illegal, trade in European eel, covered up by incomplete or fraudulent labelling. This calls into question the compliance with existing national and international trade regulations and its implications for food safety and sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".